Visual Computing

University of Konstanz
ACM Transactions on Graphics

Low-discrepancy Blue Noise Sampling

A. Ahmed, H. Perrier, D. Coeurjolly, V. Ostromoukhov, J. Guo, D. Yan, H. Huang, O. Deussen
Teaser of Low-discrepancy Blue Noise Sampling


Paper (.pdf, 13.2 MB)


We present a novel technique that produces two-dimensional lowdiscrepancy (LD) blue noise point sets for sampling. Using onedimensional binary van der Corput sequences, we construct twodimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.


  acmid      = {2980218},
  address    = {New York, NY, USA},
  articleno  = {247},
  author     = {A. Ahmed and H. Perrier and D. Coeurjolly and V. Ostromoukhov and J. Guo and D. Yan and H. Huang and O. Deussen},
  doi        = {10.1145/2980179.2980218},
  issn       = {0730-0301},
  issue_date = {November 2016},
  journal    = {ACM Transactions on Graphics},
  keywords   = {blue noise, low discrepancy, monte carlo, quasi-monte carlo, sampling},
  month      = {nov},
  number     = {6},
  numpages   = {13},
  pages      = {247:1--247:13},
  publisher  = {ACM},
  title      = {Low-discrepancy Blue Noise Sampling},
  volume     = {35},
  year       = {2016},
  url        = {},